Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Screening of material defects using universal machine-learning interatomic potentials
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2504.06993.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:13:43.830850Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:37:19.150010Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 00f9cf5b-d2b4-4b9f-8297-57492c9c5ddb · inbound
High-performance training and inference for deep equivariant interatomic potentials cites this paper.
High-performance training and inference for deep equivariant interatomic potentials Screening of material defects using universal machine-learning interatomic potentials
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:13:43.830850Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47a399e2-b468-4005-b809-a7112365ae52 · inbound
Accelerating point defect simulations using data-driven and machine learning approaches cites this paper.
Accelerating point defect simulations using data-driven and machine learning approaches Screening of material defects using universal machine-learning interatomic potentials
Reference 103
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:06:05.132151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 66f5d0b9-3c85-4d10-8bb4-3413e2be0b3c · inbound
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Screening of material defects using universal machine-learning interatomic potentials
Reference 173
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:37:19.151787Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.